from deep_translator import GoogleTranslator
from datetime import datetime as dt
import matplotlib.pyplot as plt
import matplotlib.font_manager
import pandas as pd
import numpy as np
import folium as f
import openpyxl
import json
%matplotlib inline

output_path = 'output'
geojson_path = 'maps/world/world-administrative-boundaries.geojson'

def write_csv(df, path):
    df.to_csv(path, index=True, header=True)
    
def open_csv(path):
    return pd.read_csv(path)

def open_json(path):
    return json.load(open(path))

def get_date():
    return str(dt.now().strftime("%Y-%m-%d-%H-%M-%S"))

def save_map(m):
    m.save(f"{output_path}\map.html")

def save_plot(plt, direction):
    return "Image saved" if plt.savefig(f'{output_path}\\{direction}_barplot-{get_date()}.png', dpi=1200, bbox_inches='tight') else None

# Reading
path = 'raw_data/Perfil_EmpresasImpo_2022_WEB.xlsx'
df = pd.read_excel(path, engine='openpyxl')

# Cleaning 
df = pd.DataFrame(df['País_origen'].value_counts())
df.dropna()

# Modificaré las columnas con rename(), así que no coloco los países como índices.
df = df.reset_index()

# Filtering
mask = (df['País_origen'] == 'Sin información') | (df['País_origen'] == 'República Dominicana')
df = df[~mask]
df.rename(columns={"País_origen":"Country", "count":"Total_imports"}, inplace=True)
df

	Country	Total_imports
0	China	203794
1	Estados Unidos de América	154279
2	España	25455
3	Italia	17690
4	México	17593
...	...	...
186	San Cristóbal y Nieves	1
187	Brunéi	1
188	Libia	1
189	Burkina Faso	1
190	Islas Åland	1

190 rows × 2 columns

# Pruebas con los nombres de algunos países # dictionary={'ä':'a','ö':'o','Ä':'A','å':'a', 'Å': 'a'} # df.replace(dictionary, regex=True, inplace=True)

# Country column translation in order to match geojson countries for displaying the data on the map.
df['Country'] = df['Country'].apply(lambda x: str(GoogleTranslator(source='es', target='en').translate(x)))
df['Country'] = df['Country'].replace('The Savior', 'El Salvador')
df

	Country	Total_imports
0	China	203794
1	United States of America	154279
2	Spain	25455
3	Italy	17690
4	Mexico	17593
...	...	...
186	Saint Kitts and Nevis	1
187	Brunei	1
188	Libya	1
189	Burkina Faso	1
190	Aland Islands	1

190 rows × 2 columns

# Setting Country as the DataFrame index.
df.set_index('Country', inplace=True)
df

	Total_imports
Country
China	203794
United States of America	154279
Spain	25455
Italy	17690
Mexico	17593
...	...
Saint Kitts and Nevis	1
Brunei	1
Libya	1
Burkina Faso	1
Aland Islands	1

190 rows × 1 columns

# Saving processed data
save_path = f'{output_path}/countries_imports.csv'
write_csv(df, save_path)

# Ahora trabajaré con la data procesada.
df = open_csv(save_path)
df.set_index('Country', inplace=True)

# Organizamos la data de manera descendente.
df = df.sort_values(by='Total_imports', ascending=False)
df

	Total_imports
Country
China	203794
United States of America	154279
Spain	25455
Italy	17690
Mexico	17593
...	...
Tuvalu	1
Mali	1
South Georgia and the South Sandwich Islands	1
Montserrat	1
Aland Islands	1

190 rows × 1 columns

# Horizontal Bars Plot
fig, ax = plt.subplots()
bp = df[:10] # Top 10

# Save the chart so we can loop through the bars below.
bars = ax.bar(
    x=np.arange(bp.size),
    height=bp['Total_imports'],
    tick_label=bp.index
)

# Axis formatting.
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.spines['left'].set_visible(False)
ax.spines['bottom'].set_color('#DDDDDD')
ax.tick_params(bottom=False, left=False)
ax.set_axisbelow(True)
ax.yaxis.grid(True, color='#EEEEEE')
ax.xaxis.grid(False)

# Add text annotations to the top of the bars.
bar_color = bars[0].get_facecolor()
for bar in bars:
  ax.text(
      bar.get_x() + bar.get_width() / 2,
      bar.get_height() + 0.3,
      round(bar.get_height(), 1),
      horizontalalignment='center',
      verticalalignment='bottom',
      color=bar_color,
      weight='bold'
  )

# Add labels and a title.
ax.set_xlabel('País de origen (p)', labelpad=15, color='#333333')
ax.set_ylabel('Cantidad de importaciones (n)', labelpad=15, color='#333333')
ax.set_title('Importaciones de mercancía [2022]', pad=15, color='#333333',
             weight='bold')

ax.margins(0.01, None)
fig.autofmt_xdate()
fig.tight_layout()
save_plot(fig, 'vertical')

# Ordenamos de manera ascendente para graficar de abajo hacia arriba.
bp = bp.sort_values(by='Total_imports', ascending=True)

# Spliting data for x and y.
names = bp.index
values = bp['Total_imports']
lim = bp['Total_imports'][-1] * 1.1

# Setting font
plt.rcParams['font.family'] = 'DejaVu Sans'
plt.rcParams['font.sans-serif'] = 'DejaVu Sans'

# Set the style of the axes and the text color
plt.rcParams['axes.edgecolor']='#333F4B'
plt.rcParams['axes.linewidth']=0.8
plt.rcParams['xtick.color']='#333F4B'
plt.rcParams['ytick.color']='#333F4B'
plt.rcParams['text.color']='#333F4B'

# Numeric placeholder for the y axis
my_range=list(range(1, len(bp.index)+1))

fig, ax = plt.subplots(figsize=(5,3.5))

# Create for each expense type an horizontal line that starts at x = 0 with the length 
# represented by the specific expense percentage value.
plt.hlines(y=my_range, xmin=0, xmax=bp['Total_imports'], color='#007ACC', alpha=0.2, linewidth=5)

# create for each expense type a dot at the level of the expense percentage value
plt.plot(bp['Total_imports'], my_range, "o", markersize=5, color='#007ACC', alpha=0.6)

# set labels
ax.set_xlabel('Importaciones', fontsize=15, fontweight='black', color = '#333F4B')
ax.set_ylabel('')

# set axis
ax.tick_params(axis='both', which='major', labelsize=12)
plt.yticks(my_range, bp.index)

# add an horizonal label for the y axis 
fig.text(-0.23, 0.96, 'País de Origen', fontsize=15, fontweight='black', color = '#333F4B')

# change the style of the axis spines
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)

ax.spines['left'].set_bounds((1, len(my_range)))
ax.set_xlim(0, lim)

ax.spines['left'].set_position(('outward', 8))
ax.spines['bottom'].set_position(('outward', 5))

save_plot(plt, 'horizontal')

# Displaying the data on the map.
center = [35.762887,84.083132]
mapa = f.Map(location=center, zoom_start=2,
         min_zoom=1, max_bounds=True,
         min_lat=-84, min_lon=-175,
         max_lat=84,  max_lon=187,
        control_scale=True
    )

file_json = geojson_path
data_geojson = open_json(file_json)

f.Choropleth(geo_data=data_geojson,
                name = 'Importaciones',
                data=df,
                columns=(df.index, 'Total_imports'),
                key_on="properties.name",
                fill_color="Pastel2",
                fill_opacity=.7,
                line_opacity=.1,
                nan_fill_color='grey',
                nan_fill_opacity=0.1,
                legend_name='Países que exportaron mercancía hacia República Dominicana (2022)',
                highlight= True,
                reset=True
).add_to(mapa)

f.LayerControl().add_to(mapa)
mapa

Make this Notebook Trusted to load map: File -> Trust Notebook

save_map(mapa)

print("Total de paises que importaron a República Dominicana en 2022:", len(df.index)) 

Total de paises que importaron a República Dominicana en 2022: 190